UC Santa Barbara

ABSTRACT: Linear scattering of ocean wave energy at the ocean–continent transition structure causes the primary microseism at a period of 14 s. Subsequent nonlinear wave–wave interactions produce the secondary microseism signal at half the primary microseism period (Longuet-Higgins, 1950; Haubrich et al., 1963). We use three years (2018–2022) of seismic data from an ongoing microarray deployment in the UC Santa Barbara Sedgwick Reserve, situated in the Santa Ynez Valley, to constrain seasonal and long-term microseismic noise characteristics for this portion of California’s central coast. Ancillary buoy data (spectral data, wave height, wind speed and direction) from the National Oceanic and Atmospheric Administration are used to explore the causal relationship between ocean swell and the generation of microseisms. This region is found to exhibit strong seasonality in the primary and secondary microseism bands (0.05–0.1 and 0.1–0.3 Hz, respectively), with much higher noise levels in the winter compared with the summer, especially for the secondary microseism (15.4 dB). We also observe a systematic shift in the peak frequency of the secondary microseism between the winter (∼0.14 Hz) and summer (∼0.20 Hz) months, which may reflect a difference in sources of secondary microseisms between the two seasons. Local buoy wave height and spectral data are well correlated with seismic power spectra during times of incoming storm swell in winter, indicating locally generated microseisms along the central coast during this season.

It is well established that a variety of physical distancing measures are invaluable as part of the overall response to pandemics. COVID-19 is the most recent such pandemic, a respiratory disease transmitted through interaction, necessitating steps to minimize or eliminate the potential for exposure. Of course, this is driven by a desire to keep the economy moving, allow for social activity, continue education, support the livelihoods of individuals, etc. Regional science and supporting analytics have an important role in managing activity through the development and application of methods that enable spatial interaction that mitigates transmission. This paper details methods to plan for physical distancing at micro-scales, enabling the return of social, economic, entertainment, etc. activities. Geographic information systems combined with spatial optimization offers important spatial coronametrics for the mitigation of risk in disease transmission. Applications detailing office space occupancy and travel along with room seating are highlighted.

The stellar optoelectronic properties of metal halide perovskites provide enormous promise for next-generation optical devices with excellent conversion efficiencies and lower manufacturing costs. However, there is a long-standing ambiguity as to whether the perovskite surface/interface (e.g. structure, charge transfer or source of off-target recombination) or bulk properties are the more determining factor in device performance. Here we fabricate an array of CsPbI3 crystal and hybrid glass composites by sintering and globally visualise the property-performance landscape. Our findings reveal that the interface is the primary determinant of the crystal phases, optoelectronic quality, and stability of CsPbI3. In particular, the presence of a diffusion alloying layer is discovered to be critical for passivating surface traps, and beneficially altering the energy landscape of crystal phases. However, high-temperature sintering results in the promotion of a non-stoichiometric perovskite and excess traps at the interface, despite the short-range structure of halide is retained within the alloying layer. By shedding light on functional hetero-interfaces, our research offers the key factors for engineering high-performance perovskite devices.

Carbon dioxide and methane emissions are the two primary anthropogenic climate-forcing agents and an important source of uncertainty in the global carbon budget. Uncertainties are further magnified when emissions occur at fine spatial scales (<1 km), making attribution challenging. We present the first observations from NASAs Earth Surface Mineral Dust Source Investigation (EMIT) imaging spectrometer showing quantification and attribution of fine-scale methane (0.3 to 73 tonnes CH4 hour-1) and carbon dioxide sources (1571 to 3511 tonnes CO2 hour-1) spanning the oil and gas, waste, and energy sectors. For selected countries observed during the first 30 days of EMIT operations, methane emissions varied at a regional scale, with the largest total emissions observed for Turkmenistan (731 ± 148 tonnes CH4 hour-1). These results highlight the contributions of current and planned point source imagers in closing global carbon budgets.

The synthetic utility of heterotelechelic polydimethylsiloxane (PDMS) derivatives is limited due to challenges in preparing materials with high chain-end fidelity. In this study, anionic ring-opening polymerization (AROP) of hexamethylcyclotrisiloxane (D3) monomers using a specifically designed silyl hydride (Si-H)-based initiator provides a versatile approach toward a library of heterotelechelic PDMS polymers. A novel initiator, where the Si-H terminal group is connected to a C atom (H-Si-C) and not an O atom (H-Si-O) as in traditional systems, suppresses intermolecular transfer of the Si-H group, leading to heterotelechelic PDMS derivatives with a high degree of control over chain ends. In situ termination of the D3 propagating chain end with commercially available chlorosilanes (alkyl chlorides, methacrylates, and norbornenes) yields an array of chain-end-functionalized PDMS derivatives. This diversity can be further increased by hydrosilylation with functionalized alkenes (alcohols, esters, and epoxides) to generate a library of heterotelechelic PDMS polymers. Due to the living nature of ring-opening polymerization and efficient initiation, narrow-dispersity (Đ < 1.2) polymers spanning a wide range of molar masses (2-11 kg mol-1) were synthesized. With facile access to α-Si-H and ω-norbornene functionalized PDMS macromonomers (H-PDMS-Nb), the synthesis of well-defined supersoft (G = 30 kPa) PDMS bottlebrush networks, which are difficult to prepare using established strategies, was demonstrated.

Gaussian Graphical models (GGM) are widely used to estimate network structure in domains ranging from biology to finance. In practice, data is often corrupted by latent confounders which biases inference of the underlying true graphical structure. In this paper, we compare and contrast two strategies for inference in graphical models with latent confounders: Gaussian graphical models with latent variables (LVGGM) and PCA-based removal of confounding (PCA+GGM). While these two approaches have similar goals, they are motivated by different assumptions about confounding. In this paper, we explore the connection between these two approaches and propose a new method, which combines the strengths of these two approaches. We prove the consistency and convergence rate for the PCA-based method and use these results to provide guidance about when to use each method. We demonstrate the effectiveness of our methodology using both simulations and two real-world applications.

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Research and theory in nonverbal communication have made great advances toward understanding the patterns and functions of nonverbal behavior in social settings. Progress has been hindered, we argue, by presumptions about nonverbal behavior that follow from both received wisdom and faulty evidence. In this article, we document four persistent misconceptions about nonverbal communication-namely, that people communicate using decodable body language; that they have a stable personal space by which they regulate contact with others; that they express emotion using universal, evolved, iconic, categorical facial expressions; and that they can deceive and detect deception, using dependable telltale clues. We show how these misconceptions permeate research as well as the practices of popular behavior experts, with consequences that extend from intimate relationships to the boardroom and courtroom and even to the arena of international security. Notwithstanding these misconceptions, existing frameworks of nonverbal communication are being challenged by more comprehensive systems approaches and by virtual technologies that ambiguate the roles and identities of interactants and the contexts of interaction.

Processing facial expressions of emotion draws on a distributed brain network. In particular, judging ambiguous facial emotions involves coordination between multiple brain areas. Here, we applied multimodal functional connectivity analysis to achieve network-level understanding of the neural mechanisms underlying perceptual ambiguity in facial expressions. We found directional effective connectivity between the amygdala, dorsomedial prefrontal cortex (dmPFC), and ventromedial PFC, supporting both bottom-up affective processes for ambiguity representation/perception and top-down cognitive processes for ambiguity resolution/decision. Direct recordings from the human neurosurgical patients showed that the responses of amygdala and dmPFC neurons were modulated by the level of emotion ambiguity, and amygdala neurons responded earlier than dmPFC neurons, reflecting the bottom-up process for ambiguity processing. We further found parietal-frontal coherence and delta-alpha cross-frequency coupling involved in encoding emotion ambiguity. We replicated the EEG coherence result using independent experiments and further showed modulation of the coherence. EEG source connectivity revealed that the dmPFC top-down regulated the activities in other brain regions. Lastly, we showed altered behavioral responses in neuropsychiatric patients who may have dysfunctions in amygdala-PFC functional connectivity. Together, using multimodal experimental and analytical approaches, we have delineated a neural network that underlies processing of emotion ambiguity.